ArticleBioinformatics (Oxford, England)2023
S1000: a better taxonomic name corpus for biomedical information extraction.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MilkOligoCorpus: A semantically annotated resource for knowledge extraction on mammalian milk oligosaccharides.PloS one · 2025Article
- Lifestyle factors in the biomedical literature: an ontology and comprehensive resources for named entity recognition.Bioinformatics (Oxford, England) · 2024Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Improving dictionary-based named entity recognition with deep learning.Bioinformatics (Oxford, England) · 2024Article
- BELHD: improving biomedical entity linking with homonym disambiguation.Bioinformatics (Oxford, England) · 2024Article
- CoNECo: a Corpus for Named Entity recognition and normalization of protein Complexes.Bioinformatics advances · 2024Article
- TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature.PloS one · 2024Article
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Authors and funding
7 authors.
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Abstract
motivationThe recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learning-based methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize that this is primarily due to the lack of appropriate corpora.
resultsWe introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that S1000 makes highly accurate recognition of species names possible (F-score =93.1%), both for deep learning and dictionary-based methods. AVAILABILITY AND IMPLEMENTATION: All resources introduced in this study are available under open licenses from https://jensenlab.org/resources/s1000/. The webpage contains links to a Zenodo project and three GitHub repositories associated with the study.
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